Shinya Kimura
Impact in
- Hematology top 0.2%
- Chronic Myeloid Leukemia Treatments
- Acute Myeloid Leukemia Research
- Genetics top 0.5%
- Chronic Lymphocytic Leukemia Research
Papers in
- Hematology 182
- Chronic Myeloid Leukemia Treatments 131
- Acute Myeloid Leukemia Research 37
- Genetics 110
- Chronic Lymphocytic Leukemia Research 86
- Co-authors
- Taira Maekawa (99 shared papers)Junya Kuroda (50 shared papers)Eishi Ashihara (60 shared papers)Takeshi Yuasa (27 shared papers)Naoko Sueoka‐Aragane (80 shared papers)Andrew W. Roberts (3 shared papers)Asumi Yokota (34 shared papers)Hiroshi Nojima (6 shared papers)
- Journals
- Blood (43 papers)International Journal of Hematology (22 papers)PLoS ONE (10 papers)Cancer Science (10 papers)Molecular Cancer Therapeutics (7 papers)
- Partner nations
- JapanUnited StatesAustralia
In The Last Decade
Shinya Kimura
413 papers receiving 7.2k citations
Peers
Comparison fields: 5 of 152
- Hematology 2.0k
- Genetics 1.2k
- Oncology 1.8k
- Immunology 925
- Rheumatology 606
Countries citing papers authored by Shinya Kimura
This map shows the geographic impact of Shinya Kimura's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shinya Kimura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shinya Kimura more than expected).
Fields of papers citing papers by Shinya Kimura
This network shows the impact of papers produced by Shinya Kimura. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shinya Kimura. The network helps show where Shinya Kimura may publish in the future.
Co-authors
The 25 scholars most cited alongside Shinya Kimura, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 433 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1998 | 324 | |
| 2 | 2006 | 284 | |
| 3 | 2005 | 206 | |
| 4 | 2005 | 170 | |
| 5 | 2008 | 162 | |
| 6 | 2005 | 128 | |
| 7 | 2001 | 128 | |
| 8 | 2004 | 113 | |
| 9 | 1997 | 107 | |
| 10 | 2006 | 83 | |
| 11 | 2017 | 79 | |
| 12 | 2004 | 74 | |
| 13 | 2014 | 72 | |
| 14 | 2007 | 71 | |
| 15 | 2002 | 71 | |
| 16 | 2015 | 71 | |
| 17 | 2008 | 71 | |
| 18 | 2007 | 71 | |
| 19 | 2013 | 69 | |
| 20 | 2006 | 68 |
About Shinya Kimura
Shinya Kimura is a scholar working on Hematology, Genetics, Oncology, Molecular Biology and Pulmonary and Respiratory Medicine, having authored 433 papers that have together received 7.4k indexed citations. Recurring topics across this work include Chronic Myeloid Leukemia Treatments (131 papers), Chronic Lymphocytic Leukemia Research (86 papers), Eosinophilic Disorders and Syndromes (47 papers), Acute Myeloid Leukemia Research (37 papers), Lung Cancer Treatments and Mutations (28 papers), Lymphoma Diagnosis and Treatment (26 papers), T-cell and Retrovirus Studies (20 papers) and Acute Lymphoblastic Leukemia research (18 papers). The work is most often cited by research in Hematology (2.0k citations), Genetics (1.2k citations), Oncology (1.8k citations), Immunology (925 citations) and Rheumatology (606 citations). Shinya Kimura has collaborated with scholars based in Japan, United States and Australia. Frequent co-authors include Taira Maekawa, Junya Kuroda, Eishi Ashihara, Takeshi Yuasa, Naoko Sueoka‐Aragane, Andrew W. Roberts, Asumi Yokota, Hiroshi Nojima, Hidekazu Segawa and Warren S. Alexander. Their work appears in journals such as Blood, International Journal of Hematology, PLoS ONE, Cancer Science and Molecular Cancer Therapeutics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.